Generative Video Ad Insertion via Scene Analysis
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Solution Overview
Problem
Current media content distribution systems cannot dynamically modify video content in real-time to tailor it to individual users, leading to a poor user experience and missed opportunities for advertisers, especially with premium content protected by DRM restrictions.
Innovation Solution
A generative content infusion system using AI and machine learning techniques, such as GANs and VAEs, to personalize video content on-demand by analyzing user behavior and context, allowing for real-time generation and modification of advertisements and video content within the existing DRM framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If advertisements are displayed to users during media content playback, then advertisers can reach targeted users, but the flow of watched content is broken and user experience deteriorates
Solution Approach 1:
The patent extracts the advertisement content from the original video feed and generates it separately using generative AI models. Instead of interrupting the content flow with pre-produced ads, the system generates ads on-demand and seamlessly integrates them into the video stream, maintaining continuous playback without breaking the user's viewing experience.
Solution Approach 2:
The patent introduces an intermediary layer between the content source and the user display. This intermediary system uses generative AI to create personalized advertisements in real-time and dynamically inserts them into the video stream at appropriate moments, allowing ad delivery without disrupting the original content flow or requiring manual intervention.
2Loss of information
If advertisements are personalized for individual users, then user engagement increases, but real-time content modification capability is required which current systems lack
Solution Approach 1:
The patent transforms the static, pre-produced advertisement model into a dynamic system where advertisements are generated in real-time based on user context, behavior, and preferences. The generative AI models continuously adapt and create personalized ad content on-demand, enabling the system to respond dynamically to changing user states without requiring offline production for each user.
Solution Approach 2:
The patent changes the fundamental parameters of advertisement delivery by shifting from fixed, pre-produced content to dynamically generated content with variable parameters such as user demographics, viewing context, and real-time behavior. This allows the system to modify advertisement content parameters in real-time based on user-specific factors, achieving personalization at scale.
3Manufacturing precision
If video content is generated offline, then production quality can be maintained, but no opportunity exists for users to influence content in real time
Solution Approach 1:
The patent applies preliminary action by pre-training generative AI models with high-quality production standards and content templates before real-time operation. These models are prepared in advance with the necessary knowledge of production quality requirements, allowing them to generate personalized content in real-time while maintaining professional production standards without requiring offline generation for each user.
Solution Approach 2:
The patent uses copying by creating synthetic advertisement content that mimics the style, quality, and format of professionally produced videos. The generative AI models replicate production quality characteristics from template content while generating unique personalized advertisements, achieving both high production standards and real-time user-specific customization.
4Ease of manufacture
If generative AI models are used to create video content, then production costs are reduced and personalization is enabled, but computational resources and time are required for real-time generation
Solution Approach 1:
The patent performs preliminary action by pre-training generative AI models with extensive video content and production templates before deployment. This pre-training phase, which occurs offline, enables the models to generate high-quality personalized advertisements rapidly during real-time operation, reducing the computational time required during actual content delivery while maintaining production quality.
Solution Approach 2:
The patent implements dynamics by using lightweight generative models that can quickly adapt and generate content in real-time. The system dynamically adjusts the level of generation complexity based on available computational resources and time constraints, enabling cost-effective personalization without excessive generation delays during video playback.
Data Source
AI summary
Various embodiments for dynamically generating an advertisement in a video stream are disclosed. In one embodiment, video stream content associated with a video stream for a user device is received. Video analytics data is obtained for the video stream content, which indicates a scene recognized in the video stream content. An advertisement to be generated and inserted into the video stream content is then selected based on the scene recognized in the video stream content, and an advertisement template for generating the selected advertisement is obtained. Video advertisement content corresponding to the advertisement is then generated based on the advertisement template and the video analytics data. The video advertisement content is then inserted into the video stream content, and the modified video stream content is transmitted to the user device.